• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.294-295
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• 2004

• The Journal of Society for e-Business Studies
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• v.17 no.3
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• pp.15-41
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• 2012

Recently, there happens many purchasing cases encouraged by friends' recommendation in SNS (Social Network Service). This study investigates the effect of friend recommendation on consumers' purchasing heuristic. For this purpose, we compare the effect of friend recommendation with consumer recommendation in terms of trustworthy, specialty, relevancy. Usually, the frequency of friend recommendation is far lower than that of consumer recommendation. Hence, we examine how the effect of information source (friend recommendation or consumer recommendation) is moderated by the frequency of recommendation, as well. As results, this study finds out that, under the same frequency, friend recommendation does not have significantly stronger effect on the purchasing heuristic, although friend recommendation is evidenced as one of significant heuristic inducers. However, in terms of trustworthy, friend recommendation is significantly superior to the consumer recommendation. Moreover, under sufficiently higher frequency, friend recommendation works as better heuristic factor than consumer recommendation. The results deliver managerial implications in the perspective of understanding consumers' purchasing decisions and responding strategies of firms.

• Journal of Smart Tourism
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• v.2 no.4
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• pp.35-43
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• 2022

Tourism businesses use chat services to provide immediate customer support and to help users navigate within a website, but there are more outcomes of this interaction that should be examined. The current study aimed to discover if the online travel chat service quality and information value of the online travel chat service lead to user satisfaction with the service and visit intention to a recommended destination by Korea Tourism Organization's 1330 Live Chat. The results indicate that information value (functional and innovation) and online travel chat service quality (reliability, assurance, and security) lead to satisfaction with the live chat service and visit intention to a recommended destination. The results can benefit practitioners who want to expand and improve their customer service interaction and recommendations, and to scholars who study the relationship between customer services in tourism recommendation and sales context.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.547-555
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• 2013

This study aims to examine association between festival service evaluation attribute and behavior intention of visitors and research satisfaction with festival, second visit and recommendation intention, ultimately in order to suggest measures to establish market strategies. The study was conducted as follows. First, a total of 360 pieces of questionnaire were distributed from October 14 to 16, 2011 and a total of 335 pieces were collected. Except 15 pieces without responses, 320 were used for the study. Second, in service evaluation elements, program, facility and performance review had positive impacts on the satisfaction and second visit. All evaluation elements also positively affected recommendation intention. Third, in association between demographic features and satisfaction, second visit and recommendation intention, while the satisfaction positively influenced bringing a friend, it negatively influenced academic background and income. In addition, residence and job gave a positive affect on second visit, while income, bringing family and first visit gave a negative effect on the second visit. Last, age, academic background, income and bringing family gave a negative effect on recommendation intention.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.265-273
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• 2012

It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also proposed the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1561-1566
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• 2017

Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.257-264
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• 2012

The development of mobile devices and the spread of wireless network help share and exchange information and resources more easily. The bond them to Cloud Computing technology help pay attention to "Mobile Cloud" service, so there have been being a lot of studies on "Mobile Cloud" service. Especially, the important of 'Recommendation Service' which is customized for each user's preference and context has been increasing. In order to provide appropriate recommendation services, it enables to recognize user's current state, analyze the user's profile like user's tendency and preference, and draw the service answering the user's request. Most existing frameworks, however, are not very suitable for mobile devices because they were proposed on the web-based. And other context information except location information among user's context information are not much considered. Therefore, this paper proposed the context-aware framework, which provides more suitable services by using user's context and profile.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.59-72
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• 2004

The range of Internet usage is drastically broadened and diversed from information retrieval and collection to many different functions. Contrasting to the increase of Internet use, the efficiency of finding necessary information is decreased. Therefore, the need of information system which provides customized information is emerged. Our research proposes Web Service based recommendation system which employes inference engine to find and recommend the most appropriate products for users. Web applications in present provide useful information for users while they still carry the problem of overcoming different platforms and distributed computing environment. The need of standardized and systematic approach is necessary for easier communication and coherent system development through heterogeneous environments. Web Service is programming language independent and improves interoperability by describing, deploying, and executing modularized applications through network. The paper focuses on developing Web Service based recommendation system which will provide benchmarks of Web Service realization. It is done by integrating inference engine where the dynamics of information and user preferences are taken into account.